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Bridge the Capabilities of AI with the Needs of Human Users

Current machine learning (ML) methods are usually developed via a data-centric approach regardless of the usage context and the end users. While such a one-size-fits-all strategy ensures these algorithms are generic and applicable to a variety of domain problems, it also poses usability challenges for domain users in interpreting model results, obtaining actionable insights, and collaborating with AI in decision-making and knowledge discovery. To facilitate the application of ML and improve its usability, interactive visualization should be considered as an indispensable component. Apart from its efficiency in organizing and communicating information, interactive visualization can be disentangled from the algorithmic aspects to incorporate the perspective of users and the characteristics of the applied domains. In this talk, we demonstrate the important role of visual interfaces in the successful application of AI via real-world case studies. We summarize design guidelines for bridging the capabilities of AI with the needs of domain users.

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Qianwen WANG
InteractiveVisualizationforUsableAI
Bridgethe Capabilities of AI with
theNeedsofHumanUsers
Users
AI
1
Langer et al. 2021. What Do We Want From Explainable Artificial Intelligence
AIandHuman
AI System
2
Langer et al. 2021. What Do We Want From Explainable Artificial Intelligence
AIandHuman
AI System
3
AIandHuman
AI System
Usable
whether domain users can use AI to complete
desired tasks easily and efficiently
Langer et al. 2021. What Do We Want From Explainable Artificial Intelligence
4
AIandHuman
Usable
What makes Usable AI
AI
More accurate, stable,
and faithful algorithms
Users
Interface
Jointly consider the
capabilities of AI, the
needs of users, and the
characteristics of the
usage context
5
AIandHuman
Usable
What makes Usable AI
6
AI
AIandHuman
Usable
It is promising
Even if we were to make no further progress in the next decade,
deploying existing ML algorithms to every applicable problem would
be a game changer for most industries.
— Francois Chollet
7
AIandHuman
Usable
It is promising, but difficult
8
Epic’s AI algorithms are delivering
inaccurate information on seriously ill
patients
MIKE REDDY FOR STAT
https://www.statnews.com/2021/07/26/epic-hospital-algorithms-sepsis-investigation/?
utm_source=researcher_app&utm_medium=referral&utm_campaign=RESR_MRKT_Researcher_inbound
https://www.fiercehealthcare.com/practices/nearly-half-u-s-doctors-say-they-are-anxious-about-
using-ai-powered-software-survey
AIandHuman
Usable
Why Usable AI is hard
Users
AI
Abstract benchmark tasks Complicated domain-specific tasks
Treatment
suggestion
age
disease history
symptoms
…..
Low level of domain expertise
Which domain-related information should
be provided by the AI model?
Low level of AI expertise
How to ask about domain-related
information from the AI model?
9
Algorithm-centric User-centric
10
U
d
U
d
Stages of the
Listening Process
Receive
Understand and
Remember
Evaluate and
Feedback
Relevant information about the AI
are revealed to the users for the desired tasks
11
Receive
Users and AI can achieve a consensus
about the desired tasks
12
Relevant information about the AI
are revealed to the users for the desired tasks
Understand and
Remember
Users can provide feedback to
AI about the desired tasks
13
Users and AI can achieve a consensus about the
desired tasks
Relevant information about the AI
are revealed to the users for the desired tasks
Evaluate and
Feedback
achieve a consensus about the desired tasks
refine AI for the desired tasks
UnderstandAIintermsofperformance
Visual Genealogy of Deep Neural Networks
Qianwen Wang1, Jun Yuan2, Shuxin Chen2, Hang Su2, Huamin Qu1, and Shixia Liu2
Tshinghua
University
ATMSeer: Increasing Transparency and
Controllability in Automated Machine Learning
Qianwen Wang, Yao Ming, Zhihua Jin, Qiaomu Shen, Dongyu Liu,
Micah J. Smith, Kalyan Veeramachaneni, Huamin Qu
14
Relevant information about the AI
are revealed to the users for the desired tasks
IEEE transactions on visualization and computer graphics 26 (11), 3340-3352
Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
achieve a consensus about the desired tasks
refine AI for the desired tasks
UnderstandAIintermsoffairness
To help users better understand AI for the desired tasks
15
Tshinghua
University
1 2
Qianwen
Wang1
Zhenhua
Xu1
Huamin
Qu1
Shixia
Liu2
Zhutian
Chen1
Yong
Wang1
IEEE InfoVis 2020
VisualAnalysisofDiscrimination
inMachineLearning
Tshinghua
University
1. 2.
Qianwen
Wang1
Zhenhua
Xu1
Huamin
Qu1
Shixia
Liu2
Zhutian
Chen1
Yong
Wang1
16
What is a fair prediction?
17
A College Admission Example
50%>42% Seems unfair?
accepted females
accepted males
rejected
18
A College Admission Example
accepted females
accepted males
rejected
Low score
High score
33.3%>26.7%
75%>65%
19
A College Admission Example
20%=20%
40%=40%
60%=60%
80%=80%
Low score
High score
CS
EE CS
EE
accepted females
accepted males
rejected
20
Two individuals who are similar with
respect to a task are treated equally
21
feder-gov <8 >65
Yes 0.8
Yes 1.0
work_class education hours/week income<50
k
Conf
M
F
Discriminatory
Itemset
22
Discriminatory
Itemset
23
Challenges in Analysis
work_class
loc-gov
Education:8-12 hours/week >65
relation:
own-child
house:
rent
capital-gain:
<2000
marital:
divorced
work_class
private
Education:<12 hours/week:25-35
relation:
not-in-familiy
house:
own
work_class
private
Education:<12 hours/week:25-35
capital-gain:
2000-3000
house:
own
Long and Complex Definition
work_class
private
Education:<12 hours/week:25-35
capital-gain:
2000-3000
marital:
divorced
Intertwining
Relationship
24
Long and Complex Definition
25
Long and Complex Definition
23< raised hands < 50
Attribute Matrix
Itemset
Attribute
26
Intertwining Relationships
work_class
private
Education:<12 hours/week:25-35
relation:
not-in-familiy
house:
own
work_class
private
Education:<12 hours/week:25-35
capital-gain:
2000-3000
house:
own
work_class
private
Education:<12 hours/week:25-35
capital-gain:
2000-3000
marital:
divorced
RippleSet
27
Designing RippleSet
An item
Items set A
∈  
An item
Items set A
∈  
28
An item
Items set A
∈  
(C D)(AUBUE)
∩
(A B C D)E
∩ ∩ ∩
(A B C)(DUE)
∩ ∩
(A B E)(CUD)
∩ ∩
(B C E)(AUD)
∩ ∩
Designing RippleSet
29
An item
Items set A
∈  
(C D)(AUBUE)
∩
(A B C D)E
∩ ∩ ∩
(A B C)(DUE)
∩ ∩
(A B E)(CUD)
∩ ∩
(B C E)(AUD)
∩ ∩
ABC
ABE
BCE
ABCD
CD
Designing RippleSet
30
An item
Items set A
∈  
(C D)(AUBUE)
∩
(A B C D)E
∩ ∩ ∩
(A B C)(DUE)
∩ ∩
(A B E)(CUD)
∩ ∩
(B C E)(AUD)
∩ ∩
ABC
ABE
BCE
ABCD
CD
Items belonging to the
same set are put
together
D
D
Weighted DAG
Circle packing algorithm
Designing RippleSet
31
32
Model
Information
Design
visualizations
33
AI
Explanations
XAI
Algorithm Design
visualizations for
a specific XAI
34
AI
Explanation
XAI
Algorithm Design
visualizations for
a specific XAI
Rule-based Explanations
Ming et al. "Rulematrix: Visualizing and understanding
classifiers with rules." IEEE transactions on visualization
and computer graphics 25.1 (2018): 342-352.
Counterfactual Explanations
Chen et al. "DECE: decision explorer with counterfactual
explanations for machine learning models." IEEE
transactions on visualization and computer graphics 27.2
(2021): 1438-1447.
Attribution-based Explanations
Hohman et al. "Summit: Scaling Deep Learning
Interpretability by Visualizing Activation and Attribution
Summarizations." IEEE VAST 2019
Generaltask:ShowmeAIexplanations
35
Forcertaintasks,areaIlexplanations
equallyusableforhumanusers?
36
AIexplanationsarenotalwaysusable
Debugging Tests for Model Explanations,
NeurIPs 2020, Julius Adebayo, Michael Muelly, Ilaria Liccardi, Been Kim
Are Explanations Helpful: A Comparative Study of the Effects of Explanations in AI-
Assisted Decision-Making
IUI 2021, Xinru Wang, Ming Yin
37
Human subjects fail to identify defective
models using attribution-based
explanations, but instead rely, primarily, on
model predictions.
The explanation that is considered to resemble how
human explain decisions (i.e., counterfactual
explanation) does not improve calibrated trust.
UsableAI,itdepends
Not always work for images,
but can reveal very important insights for regulatory genomic
What works or does not is extremely domain-specific!
38
Anshul Kundaje, Stanford University
Deep learning approaches to decode the human genome
refine AI for the desired tasks
39
Users and AI can achieve a consensus about the
desired tasks
Relevant information about the AI
are revealed to the users for the desired tasks
ExtendingtheNestedModelforUser-CentricXAI:
ADesignStudyonGNN-basedDrugRepurposing
Qianwen Wang Kexin Huang Payal Chandak
Nils Gehlenborg Marinka Zitnik
HARVARD-MIT
HEALTH SCIENCES AND TECHNOLOGY
40
IEEE VIS 2022
Generaltask:
ExplainaGNNmodel
41
Generaltask:
ExplainaGNNmodel
usedfor drugrepurposing
Somemethodscanfail
GNNforDrugRepurposing
Nodes: drugs, diseases, proteins, etc

Edges: known relations among these nodes
GNN
Human 

Experts
?
42
Model for Visualization Design and Validation
43
ExtendingtheNestedModelforUser-CentricXAI
Explanation Ontology: 
A Model of Explanations for User-Centered AI
Designing Theory-Driven User-Centric Explainable AI 
!"#$%& '()*$&$+%"&
,-.+/$0+%"&
1%.2$*%3$+%"&
!"#$%&'()%#*
!"#$!%&&$'()$*")
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!"/*$)&()
!"#$%&'#()"
*"')+(",
!"1
1 2 2 drug gene/protein cellular_com.. gene/protein disease
2 drug drug disease disease disease
1 drug disease drug disease disease
Agalsidase beta chylomicron ret... Alipogene tipar... lysosomal acid l... Wolman disease
indication indication indication includes
1 drug disease gene/protein disease disease
1 drug disease gene/protein disease
Agalsidase beta
Avelumab
Idursulfase
Galsulfase
&#7/&
)-.&/0/*%#0
94#'.
)-.&/0/*%#0
45
Common Visual Presentations 

of GNN Explanations
46
Common Visual Presentations 

of GNN Explanations
47
Common Visual Presentations 

of GNN Explanations
more similar
less similar
48
c Path Explanation
E
s
c
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t
a
l
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p
r
a
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D
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s
v
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f
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x
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b
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x
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z
i
d
11 2 5 13 2
13 disease
gene/protein
molecular_function
drug
1 1 1 2 1
2
disease
gene/protein
drug
unipolar depres...
HTR7
Clozapine associated
targets
〃
HTR2C
〃
associated
targets
〃
〃
Clomipramine associated
targets
1
disease
gene/protein
pathway
drug
20 17 20 20 15 14 11 disease
gene/protein
anatomy
drug
Users can compare the
explanations of different
selected drugs
Users can hide ( ), unhide ( ), collapse ( ), or expand ( )
a group of explanation paths based on the meta-path
Drug Embedding
b
gene/protein
gene/protein
gene/protein
C3
C4
C2
Ditto mark (〃) indicates this
node is the same as the node
in the above path
a Control Panel
Select drugs
through lasso or click
M
o
c
l
o
b
e
m
i
d
e
A
g
o
m
e
l
a
t
i
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33
11
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2 3
10
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22 5 5
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23 27
12 13
1 1
1 2
C
l
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i
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5
8
3
1
1
C1 MetaMatrix provides an overview
of all predicted drugs in terms
of meta paths
C5
Ranked by scores or grouped
based on embeddings
49
50
51
52
antidiabetic drugs
53
expand/collapse hide/unhide
54
55
The visual representation of explanations matters!
User study with 12 domain experts
(physicians, senior medical students,
medical researchers)
56
0.667
0.542
0.542
0.792
0.0 0.2 0.4 0.6 0.8 1.0
path subgraph node baseline
Accuracy
58.308
92.150
92.688
18.358
0 20 40 60 80 100 120
Time(second)
3.542
3.167
2.688
2.375
1.0 2.0 3.0 4.0 5.0
Confidence
F(3,33)=3.39
p<.05
F(3,33)=6.58
p<.05
F(3,33)=24.73
p<.05
more
accurate
less
accurate
quicker slower
more
confident
less
confident
b
a
c d Significant difference
The visual representation of explanations matters!
An inappropriate
explanation is not
necessarily better than no
explanation
57
Observations
• Explanations help assess predication qualities and reveal model flaws
While users commented most explanations “make sense”, they also pointed out that some explanations are
more like “correlation”than “causation”.
We should provide testable rather than“always-look-correct”explanations
• Users desire to fix the model flaws revealed by AI
• Users want to provide more than just data labels
Instead of correcting one specific prediction, users desire to demonstrate high-level drug action mechanism
to the AI model
58
Receive Understand and
Remember
Evaluate and
Feedback
59
Users can provide feedback to AI about the
desired tasks
Users and AI can achieve a consensus about the
desired tasks
Relevant information about the AI
are revealed to the users for the desired tasks
Isthereamechanismthatenables
bothuser-centricexplanationand
effectivefeedbackatthesametime?
60
Furui Cheng, Mark S Keller, Huamin Qu, Nils Gehlenborg, Qianwen Wang
Polyphony
An Interactive Transfer Learning Framework
for Single-Cell Data Analysis
61
IEEE VIS 2022
GeneralTask:Classification
62
Training Dataset
AI
GeneralTask:
Classification
63
DomainTask:
CellTypeAnnotation
Cell Type
Annotation
Cannot be directly applied
Task:CellTypeAnnotation
Assume that are
similar to each other
Can be very
different
64
Training
Test
Labelled data
Unlabelled new data
Task:CellTypeAnnotation
65
The prediction can be inaccurate!
We need to
• Provide Explanations so that
users can know when predictions
are inaccurate
• Enable Feedback so that users
can refine the wrong predictions
Anchor
analogous cell populations across datasets
• An AI explanation that is consistent with user
workflow and mental model
• An feedback mechanism that can be used to refine
AI performance
66
InteractiveAnchors:
ProvideExplanation&EnableFeedback
InteractiveVisualizationofAnchors
Three Aspects
Reference + Query
center of reference cell set
center of query cell set
gene expression distance
A
B C
67
Polyphony
Framework
• Anchor recommendation

• Reference building with Harmony
1
Harmony (Korsunsky et al., Nature Methods, 2019)
68
• Anchor recommendation

• Reference building with Harmony

• Query cell assignment
1
anchor
Polyphony
Framework
69
• Anchor recommendation

• User feedback
2
1
Polyphony Interface
Polyphony
Framework
70
• Anchor recommendation

• User feedback 

• Model fine-tuning
3
2
1
Polyphony
Framework
71
Polyphony
Framework
• Anchor recommendation

• User feedback 

• Model fine-tuning

• Embedding updating
4
3
2
1
72
Use Cases After Refinement
Before Refinement
The reference dataset
• a plate-based protocol
• contains 7,290 cells from 32 donors
• annotated with eleven cell types
The query dataset:
• generated using a droplet-based protocol
• contains 8,391 cells from 4 donors
• Has the same cell types as the reference
73
74
75
Evaluation
Use case
76
Evaluation
Use case
After checking the marker genes, the
user confirms that these cells belong to
the pDC cell type.
77
Evaluation
Simulation Study
• Results: model performance after running four iterations (50*4 epochs)
78
The visualization should be carefully
designed by considering not only the
AI aspect but also the needs and
mental models of the users
An interactive visualization is
need to provide relevant
information to the users for
achieving the domain tasks
The visualization should
provide both user-centric
explanations and effective
feedback mechanism
Summary
79
1 2 3
Thanks!
BridgetheCapabilitiesof AI withthe
Needsof Human Users
https://qianwen.info/
qianwen_wang@hms.harvard.edu
Users
AI
InteractiveVisualizationforUsableAI
80

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